{"id":111075,"date":"2026-09-30T09:17:09","date_gmt":"2026-09-30T01:17:09","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/111075.html"},"modified":"2026-09-30T09:17:09","modified_gmt":"2026-09-30T01:17:09","slug":"%e3%80%90%e8%80%81%e8%ae%a1%e5%b8%a6%e4%bd%a0%e6%87%82ai%e7%ae%97%e6%b3%95%e3%80%9115%ef%bc%9arnn%e4%b8%8elstm%ef%bc%8c%e8%ae%a9%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e6%8b%a5%e6%9c%89%e8%ae%b0%e5%bf%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/111075.html","title":{"rendered":"\u3010\u8001\u8ba1\u5e26\u4f60\u61c2AI\u7b97\u6cd5\u301115\uff1aRNN\u4e0eLSTM\uff0c\u8ba9\u795e\u7ecf\u7f51\u7edc\u62e5\u6709\u8bb0\u5fc6\u7684\u5e8f\u5217\u9ad8\u624b"},"content":{"rendered":"<h2>\u3010\u8001\u8ba1\u5e26\u4f60\u61c2AI\u7b97\u6cd5\u301115&#xff1a;RNN\u4e0eLSTM&#xff0c;\u8ba9\u795e\u7ecf\u7f51\u7edc\u62e5\u6709\u8bb0\u5fc6\u7684\u5e8f\u5217\u9ad8\u624b<\/h2>\n<p><img decoding=\"async\" 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alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>LSTM&#xff08;\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc&#xff09;\u662fRNN\u7684\u7ecf\u5178\u5347\u7ea7\u7248&#xff0c;\u4e13\u95e8\u6cbb\u5065\u5fd8\u3002\u5b83\u7684\u6838\u5fc3\u521b\u65b0&#xff0c;\u662f\u7ed9\u7f51\u7edc\u52a0\u4e86\u51e0\u9053\u95e8&#xff0c;\u8ba9\u7f51\u7edc\u80fd\u4e3b\u52a8\u51b3\u5b9a\u4fe1\u606f\u7684\u53bb\u7559&#xff0c;\u800c\u4e0d\u662f\u88ab\u52a8\u5730\u7a00\u91ca\u8bb0\u5fc6\u3002<\/p>\n<p>\u4f60\u4e0d\u7528\u8bb0\u5177\u4f53\u7684\u6570\u5b66&#xff0c;\u6293\u4f4f\u8fd9\u51e0\u9053\u95e8\u5728\u5e72\u561b\u5c31\u884c&#xff0c;\u5b83\u4eec\u7279\u522b\u7b26\u5408\u76f4\u89c9&#xff1a;<\/p>\n<ul>\n<li>\u9057\u5fd8\u95e8&#xff1a;\u51b3\u5b9a\u628a\u65e7\u8bb0\u5fc6\u91cc\u54ea\u4e9b\u8be5\u4e22\u6389\u3002&#xff08;\u6bd4\u5982\u8bfb\u5230\u4e00\u4e2a\u65b0\u53e5\u5b50\u7684\u4e3b\u8bed&#xff0c;\u53ef\u4ee5\u628a\u4e0a\u4e2a\u53e5\u5b50\u7684\u4e3b\u8bed\u5fd8\u4e86\u3002&#xff09;<\/li>\n<li>\u8f93\u5165\u95e8&#xff1a;\u51b3\u5b9a\u5f53\u524d\u8fd9\u4e2a\u65b0\u4fe1\u606f\u91cc&#xff0c;\u54ea\u4e9b\u503c\u5f97\u8bb0\u8fdb\u8bb0\u5fc6\u3002<\/li>\n<li>\u8f93\u51fa\u95e8&#xff1a;\u51b3\u5b9a\u57fa\u4e8e\u5f53\u524d\u7684\u8bb0\u5fc6&#xff0c;\u8f93\u51fa\u4ec0\u4e48\u3002<\/li>\n<\/ul>\n<p>\u9664\u4e86\u8fd9\u51e0\u9053\u95e8&#xff0c;LSTM\u8fd8\u7ef4\u62a4\u4e86\u4e00\u6761\u4e13\u95e8\u7684\u8bb0\u5fc6\u4f20\u9001\u5e26&#xff08;\u53eb\u7ec6\u80de\u72b6\u6001&#xff09;&#xff0c;\u91cd\u8981\u7684\u4fe1\u606f\u53ef\u4ee5\u5728\u8fd9\u6761\u4f20\u9001\u5e26\u4e0a\u4e00\u8def\u7545\u901a\u5730\u4f20\u5f88\u8fdc&#xff0c;\u4e2d\u9014\u8f83\u5c11\u88ab\u7a00\u91ca\u3002\u8fd9\u5c31\u662fLSTM\u6cbb\u5065\u5fd8\u7684\u5173\u952e&#xff0c;\u91cd\u8981\u4fe1\u606f\u8d70\u4e13\u7528\u4f20\u9001\u5e26\u957f\u8ddd\u79bb\u4fdd\u5b58&#xff0c;\u52a0\u4e0a\u51e0\u9053\u95e8\u7cbe\u7ec6\u5730\u63a7\u5236\u8bb0\u4ec0\u4e48\u5fd8\u4ec0\u4e48&#xff0c;\u957f\u671f\u4f9d\u8d56\u95ee\u9898\u5c31\u5927\u5927\u7f13\u89e3\u4e86\u3002<\/p>\n<p>\u6253\u4e2a\u6bd4\u65b9&#xff1a;\u666e\u901aRNN\u50cf\u4e00\u4e2a\u8bb0\u6027\u5dee\u7684\u4eba&#xff0c;\u542c\u5230\u5565\u90fd\u5f80\u8111\u5b50\u91cc\u585e\u3001\u65e7\u7684\u5f88\u5feb\u88ab\u6324\u6389&#xff1b;LSTM\u50cf\u4e00\u4e2a\u4f1a\u8bb0\u7b14\u8bb0\u7684\u806a\u660e\u4eba&#xff0c;\u4ed6\u6709\u4e2a\u4e13\u95e8\u7684\u7b14\u8bb0\u672c&#xff08;\u4f20\u9001\u5e26&#xff09;\u8bb0\u5173\u952e\u4fe1\u606f&#xff0c;\u8fd8\u4f1a\u5224\u65ad\u54ea\u4e9b\u65b0\u4fe1\u606f\u503c\u5f97\u8bb0\u4e0b\u3001\u54ea\u4e9b\u65e7\u7b14\u8bb0\u53ef\u4ee5\u5212\u6389\u3001\u5f53\u4e0b\u8be5\u8bf4\u4ec0\u4e48\u53c2\u8003\u54ea\u4e9b\u7b14\u8bb0&#xff0c;\u6240\u4ee5\u4ed6\u80fd\u8bb0\u4f4f\u5f88\u4e45\u4ee5\u524d\u7684\u5173\u952e\u5185\u5bb9\u3002 \u524d\u9762\u6cd5\u8bed\u7684\u4f8b\u5b50&#xff0c;LSTM\u5c31\u80fd\u628a&#034;\u6cd5\u56fd&#034;\u8fd9\u4e2a\u5173\u952e\u4fe1\u606f\u8bb0\u5728\u4f20\u9001\u5e26\u4e0a&#xff0c;\u4e00\u76f4\u5e26\u5230\u586b\u7a7a\u5904\u3002<\/p>\n<p>\u8fd8\u6709\u4e2a\u53ebGRU \u7684\u53d8\u4f53&#xff0c;\u601d\u8def\u548cLSTM\u4e00\u6837&#xff0c;\u4f46\u628a\u95e8\u7b80\u5316\u4e86&#xff08;\u5408\u5e76\u6210\u4e24\u9053\u95e8&#xff09;&#xff0c;\u66f4\u8f7b\u91cf\u3001\u8bad\u7ec3\u66f4\u5feb&#xff0c;\u6548\u679c\u548cLSTM\u5e38\u5e38\u76f8\u5f53&#xff0c;\u5b9e\u9645\u4e2d\u4e24\u8005\u90fd\u5e38\u7528\u3002<\/p>\n<h3>\u4eceRNN\u5230\u6ce8\u610f\u529b&#xff1a;\u4e00\u4e2a\u91cd\u8981\u7684\u8fc7\u6e21<\/h3>\n<p>\u8fd9\u91cc\u63d2\u4e00\u6bb5\u627f\u524d\u542f\u540e\u7684\u5185\u5bb9&#xff0c;\u5b83\u662f\u7406\u89e3\u4e0b\u4e00\u4e2a\u65f6\u4ee3\u7684\u94a5\u5319\u3002\u7528RNN\u505a\u673a\u5668\u7ffb\u8bd1\u8fd9\u7c7b&#034;\u5e8f\u5217\u5230\u5e8f\u5217&#034;\u4efb\u52a1\u65f6&#xff0c;\u4eba\u4eec\u53d1\u73b0\u4e00\u4e2a\u74f6\u9888\u3002\u4f20\u7edf\u505a\u6cd5\u662f&#xff0c;\u8ba9\u4e00\u4e2aRNN&#xff08;\u7f16\u7801\u5668&#xff09;\u628a\u6574\u4e2a\u8f93\u5165\u53e5\u5b50\u8bfb\u5b8c&#xff0c;\u538b\u7f29\u6210\u4e00\u4e2a\u56fa\u5b9a\u957f\u5ea6\u7684\u8bb0\u5fc6\u5411\u91cf&#xff0c;\u518d\u8ba9\u53e6\u4e00\u4e2aRNN&#xff08;\u89e3\u7801\u5668&#xff09;\u6839\u636e\u8fd9\u4e2a\u5411\u91cf\u5410\u51fa\u7ffb\u8bd1\u3002\u95ee\u9898\u662f&#xff0c;\u628a\u4e00\u6574\u53e5\u8bdd\u7684\u5168\u90e8\u4fe1\u606f&#xff0c;\u786c\u585e\u8fdb\u4e00\u4e2a\u56fa\u5b9a\u5927\u5c0f\u7684\u5411\u91cf\u91cc&#xff0c;\u53e5\u5b50\u4e00\u957f\u5c31\u585e\u4e0d\u4e0b\u3001\u4fe1\u606f\u4e25\u91cd\u635f\u5931&#xff0c;\u5c31\u50cf\u8ba9\u4f60\u8bfb\u5b8c\u4e00\u6574\u6bb5\u8bdd\u540e\u53ea\u51c6\u8bb0\u4e00\u53e5\u8bdd\u7684\u5bb9\u91cf&#xff0c;\u518d\u590d\u8ff0\u51fa\u6765&#xff0c;\u957f\u4e86\u80af\u5b9a\u4e22\u4e09\u843d\u56db\u3002<\/p>\n<p>\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u74f6\u9888&#xff0c;\u7814\u7a76\u8005\u63d0\u51fa\u4e86\u4e00\u4e2a\u7edd\u5999\u7684\u60f3\u6cd5&#xff0c;\u6ce8\u610f\u529b\u673a\u5236\u3002\u5b83\u7684\u601d\u8def\u662f&#xff1a;\u89e3\u7801\u5668\u5728\u751f\u6210\u6bcf\u4e00\u4e2a\u8bcd\u7684\u65f6\u5019&#xff0c;\u4e0d\u518d\u53ea\u4f9d\u8d56\u90a3\u4e2a\u88ab\u538b\u7f29\u7684\u56fa\u5b9a\u5411\u91cf&#xff0c;\u800c\u662f\u53ef\u4ee5\u56de\u5934\u53bb\u770b\u8f93\u5165\u53e5\u5b50\u7684\u6240\u6709\u90e8\u5206&#xff0c;\u5e76\u628a\u6ce8\u610f\u529b\u91cd\u70b9\u653e\u5728\u5f53\u524d\u6700\u76f8\u5173\u7684\u90a3\u51e0\u4e2a\u8bcd\u4e0a\u3002 \u6bd4\u5982\u7ffb\u8bd1\u5230&#034;\u82f9\u679c&#034;\u65f6&#xff0c;\u6ce8\u610f\u529b\u5c31\u805a\u7126\u5728\u539f\u6587\u7684apple\u90a3\u4e2a\u8bcd\u4e0a\u3002<\/p>\n<p>\u6253\u4e2a\u6bd4\u65b9&#xff0c;\u8fd9\u5c31\u50cf\u4f60\u505a\u7ffb\u8bd1&#xff0c;\u4e0d\u4f1a\u8bfb\u5b8c\u5168\u6587\u540e\u51ed\u4e00\u4e2a\u6a21\u7cca\u5370\u8c61\u786c\u7ffb&#xff0c;\u800c\u662f\u7ffb\u5230\u54ea\u53e5\u5c31\u56de\u5934\u770b\u539f\u6587\u5bf9\u5e94\u7684\u90a3\u53e5&#xff0c;\u91cd\u70b9\u770b\u3001\u7cbe\u51c6\u8bd1\u3002 \u6ce8\u610f\u529b\u673a\u5236\u8ba9\u6a21\u578b\u80fd\u52a8\u6001\u5730\u6309\u9700\u53d6\u7528\u8f93\u5165\u4fe1\u606f&#xff0c;\u4e00\u4e0b\u5b50\u7a81\u7834\u4e86\u56fa\u5b9a\u5411\u91cf\u7684\u74f6\u9888&#xff0c;\u7ffb\u8bd1\u8d28\u91cf\u5927\u5e45\u63d0\u5347\u3002\u800c\u6b63\u662f\u8fd9\u4e2a\u6ce8\u610f\u529b\u673a\u5236&#xff0c;\u540e\u6765\u88ab\u53d1\u626c\u5149\u5927\u3001\u5355\u72ec\u62ce\u51fa\u6765\u5f53\u4e3b\u89d2&#xff0c;\u50ac\u751f\u4e86\u5f7b\u5e95\u6539\u53d8AI\u7684Transformer&#xff0c;\u8fd9\u662f\u540e\u8bdd&#xff0c;\u7b2c20\u7bc7\u4f1a\u91cd\u70b9\u8bb2\u3002\u4f60\u73b0\u5728\u53ea\u8981\u8bb0\u4f4f&#xff0c;\u6ce8\u610f\u529b\u8fd9\u4e2a\u9769\u547d\u6027\u7684\u60f3\u6cd5&#xff0c;\u6700\u521d\u662f\u4e3a\u4e86\u7ed9RNN\u89e3\u51b3\u8bb0\u4e0d\u4f4f\u957f\u53e5\u5b50\u7684\u6bdb\u75c5\u800c\u751f\u7684\u3002<\/p>\n<h3>\u8f93\u5165\u548c\u8f93\u51fa\u957f\u4ec0\u4e48\u6837<\/h3>\n<p>RNN\u5bb6\u65cf\u7684\u4e00\u4e2a\u5927\u4f18\u70b9\u662f\u8f93\u5165\u8f93\u51fa\u5f62\u5f0f\u7075\u6d3b&#xff0c;\u80fd\u5e94\u5bf9\u591a\u79cd\u5e8f\u5217\u4efb\u52a1&#xff1a;<\/p>\n<ul>\n<li>\u591a\u5bf9\u4e00&#xff1a;\u8f93\u5165\u4e00\u4e2a\u5e8f\u5217&#xff0c;\u8f93\u51fa\u4e00\u4e2a\u7ed3\u679c\u3002\u5982\u60c5\u611f\u5206\u6790&#xff08;\u8f93\u5165\u4e00\u53e5\u8bdd&#xff0c;\u8f93\u51fa\u6b63\u9762\u8fd8\u662f\u8d1f\u9762&#xff09;\u3002<\/li>\n<li>\u591a\u5bf9\u591a&#xff08;\u7b49\u957f&#xff09;&#xff1a;\u8f93\u5165\u5e8f\u5217&#xff0c;\u8f93\u51fa\u7b49\u957f\u5e8f\u5217\u3002\u5982\u7ed9\u6bcf\u4e2a\u8bcd\u6807\u6ce8\u8bcd\u6027\u3002<\/li>\n<li>\u591a\u5bf9\u591a&#xff08;\u4e0d\u7b49\u957f&#xff09;&#xff1a;\u8f93\u5165\u4e00\u4e2a\u5e8f\u5217&#xff0c;\u8f93\u51fa\u53e6\u4e00\u4e2a\u5e8f\u5217\u3002\u5982\u673a\u5668\u7ffb\u8bd1&#xff08;\u8f93\u5165\u4e2d\u6587\u53e5\u5b50&#xff0c;\u8f93\u51fa\u82f1\u6587\u53e5\u5b50&#xff09;&#xff0c;\u8fd9\u79cd\u53eb\u5e8f\u5217\u5230\u5e8f\u5217\u3002<\/li>\n<\/ul>\n<p>\u8f93\u5165\u662f\u6309\u987a\u5e8f\u6392\u5217\u7684\u5143\u7d20&#xff08;\u8bcd\u3001\u5b57\u3001\u65f6\u95f4\u70b9\u7684\u6570\u503c\u7b49&#xff0c;\u8bcd\u901a\u5e38\u5148\u8f6c\u6210\u5411\u91cf&#xff09;&#xff0c;\u8f93\u51fa\u770b\u5177\u4f53\u4efb\u52a1\u3002<\/p>\n<h3>\u4e0a\u4ee3\u7801&#xff1a;\u7528LSTM\u505a\u4e00\u4e2a\u5e8f\u5217\u5206\u7c7b<\/h3>\n<p>\u7528PyTorch\u642d\u4e00\u4e2aLSTM\u3002\u8f93\u5165&#xff1a;\u4e00\u6bb5\u5e8f\u5217\u3002\u8f93\u51fa&#xff1a;\u5206\u7c7b\u7ed3\u679c\u3002\u8fd9\u91cc\u6f14\u793a\u5224\u65ad\u4e00\u6bb5\u6570\u5b57\u5e8f\u5217\u6574\u4f53\u662f\u4e0a\u5347\u8fd8\u662f\u4e0b\u964d\u8d8b\u52bf\u3002<\/p>\n<p><span class=\"token comment\"># \u4f9d\u8d56&#xff1a;pip install torch<\/span><br \/>\n<span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn<\/p>\n<p><span class=\"token comment\"># \u9020\u6570\u636e:\u4e0a\u5347\u5e8f\u5217(\u6807\u7b7e1)\u548c\u4e0b\u964d\u5e8f\u5217(\u6807\u7b7e0),\u6bcf\u6761\u957f\u5ea610<\/span><br \/>\n<span class=\"token keyword\">def<\/span> <span class=\"token function\">make_data<\/span><span class=\"token punctuation\">(<\/span>n<span class=\"token operator\">&#061;<\/span><span class=\"token number\">500<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    X<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\n    <span class=\"token keyword\">for<\/span> _ <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>n<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        start <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>rand<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">if<\/span> torch<span class=\"token punctuation\">.<\/span>rand<span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&gt;<\/span> <span class=\"token number\">0.5<\/span><span class=\"token punctuation\">:<\/span><br \/>\n            seq <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>linspace<span class=\"token punctuation\">(<\/span>start<span class=\"token punctuation\">,<\/span> start <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#043;<\/span> torch<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">0.05<\/span>  <span class=\"token comment\"># \u4e0a\u5347<\/span><br \/>\n            label <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">1<\/span><br \/>\n        <span class=\"token keyword\">else<\/span><span class=\"token punctuation\">:<\/span><br \/>\n            seq <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>linspace<span class=\"token punctuation\">(<\/span>start<span class=\"token punctuation\">,<\/span> start <span class=\"token operator\">&#8211;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#043;<\/span> torch<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">*<\/span> <span class=\"token number\">0.05<\/span>  <span class=\"token comment\"># \u4e0b\u964d<\/span><br \/>\n            label <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">0<\/span><br \/>\n        X<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>seq<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span> y<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>label<span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> torch<span class=\"token punctuation\">.<\/span>stack<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>unsqueeze<span class=\"token punctuation\">(<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> torch<span class=\"token punctuation\">.<\/span>tensor<span class=\"token punctuation\">(<\/span>y<span class=\"token punctuation\">,<\/span> dtype<span class=\"token operator\">&#061;<\/span>torch<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># (n,10,1)<\/span><\/p>\n<p>X<span class=\"token punctuation\">,<\/span> y <span class=\"token operator\">&#061;<\/span> make_data<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">LSTMClassifier<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>__init__<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>lstm <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>LSTM<span class=\"token punctuation\">(<\/span>input_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> hidden_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">16<\/span><span class=\"token punctuation\">,<\/span> batch_first<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># LSTM\u5c42<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>fc <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">16<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        out<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span>h<span class=\"token punctuation\">,<\/span> c<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>lstm<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>       <span class=\"token comment\"># out\u662f\u6bcf\u4e00\u6b65\u7684\u8f93\u51fa, h\u662f\u6700\u540e\u7684\u8bb0\u5fc6<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> torch<span class=\"token punctuation\">.<\/span>sigmoid<span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>fc<span class=\"token punctuation\">(<\/span>h<span class=\"token punctuation\">[<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u7528\u6700\u540e\u7684\u8bb0\u5fc6\u505a\u5206\u7c7b<\/span><\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> LSTMClassifier<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nloss_fn <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>BCELoss<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\noptimizer <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>optim<span class=\"token punctuation\">.<\/span>Adam<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> lr<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.01<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">30<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    pred <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>X<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>squeeze<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> loss_fn<span class=\"token punctuation\">(<\/span>pred<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">)<\/span><br \/>\n    optimizer<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span> loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span> optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u53cd\u5411\u4f20\u64ad\u56db\u6b65<\/span><br \/>\n    <span class=\"token keyword\">if<\/span> <span class=\"token punctuation\">(<\/span>epoch <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">%<\/span> <span class=\"token number\">10<\/span> <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        acc <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>pred <span class=\"token operator\">&gt;<\/span> <span class=\"token number\">0.5<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#061;&#061;<\/span> y<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>mean<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u7b2c<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>epoch<span class=\"token operator\">&#043;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">\u8f6e \u635f\u5931<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>loss<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.3f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\"> \u51c6\u786e\u7387<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>acc<span class=\"token punctuation\">.<\/span>item<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.3f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u8fd0\u884c\u8f93\u51fa&#xff08;\u793a\u4f8b&#xff0c;\u8bad\u7ec3\u6709\u968f\u673a\u6027&#xff0c;\u6570\u503c\u6bcf\u6b21\u4e0d\u540c&#xff09;&#xff1a;<\/p>\n<p>\u7b2c10\u8f6e \u635f\u59310.412 \u51c6\u786e\u73870.902<br \/>\n\u7b2c20\u8f6e \u635f\u59310.198 \u51c6\u786e\u73870.976<br \/>\n\u7b2c30\u8f6e \u635f\u59310.107 \u51c6\u786e\u73870.994<\/p>\n<p>\u8fd0\u884c\u4f60\u4f1a\u770b\u5230\u51c6\u786e\u7387\u5feb\u901f\u5347\u9ad8\u3002\u6838\u5fc3\u662fnn.LSTM\u90a3\u4e00\u5c42&#xff0c;\u5b83\u6309\u987a\u5e8f\u8bfb\u5165\u5e8f\u5217\u7684\u6bcf\u4e2a\u6570\u503c\u3001\u7ef4\u62a4\u8bb0\u5fc6&#xff0c;\u6700\u540e\u7528\u79ef\u7d2f\u7684\u8bb0\u5fc6&#xff08;h&#xff09;\u5224\u65ad\u6574\u4f53\u8d8b\u52bf\u3002\u8bad\u7ec3\u8fd8\u662f\u719f\u6089\u7684\u53cd\u5411\u4f20\u64ad\u56db\u6b65&#xff0c;\u8bf4\u660eLSTM\u4e5f\u53ea\u662f\u4e00\u79cd\u7279\u6b8a\u7684\u7f51\u7edc\u5c42&#xff0c;\u5b66\u4e60\u673a\u5236\u4e0d\u53d8\u3002<\/p>\n<h3>\u5173\u952e\u6982\u5ff5<\/h3>\n<ul>\n<li>hidden_size&#xff08;\u9690\u85cf\u72b6\u6001\u5927\u5c0f&#xff09;&#xff1a;\u8bb0\u5fc6\u7684\u5bb9\u91cf&#xff0c;\u8d8a\u5927\u80fd\u8bb0\u7684\u8d8a\u591a\u4e5f\u8d8a\u91cd\u3002<\/li>\n<li>\u5e8f\u5217\u957f\u5ea6&#xff1a;\u4e00\u6b21\u5904\u7406\u591a\u957f\u7684\u5e8f\u5217&#xff0c;\u592a\u957f\u666e\u901aRNN\u625b\u4e0d\u4f4f\u3001LSTM\u4e5f\u6709\u6781\u9650\u3002<\/li>\n<li>\u53cc\u5411RNN&#xff1a;\u8ba9\u7f51\u7edc\u65e2\u4ece\u524d\u5f80\u540e\u8bfb\u3001\u4e5f\u4ece\u540e\u5f80\u524d\u8bfb&#xff0c;\u7efc\u5408\u4e24\u4e2a\u65b9\u5411\u7684\u4fe1\u606f&#xff0c;\u5f88\u591a\u4efb\u52a1\u4e0a\u66f4\u5f3a\u3002<\/li>\n<li>\u68af\u5ea6\u6d88\u5931\u548c\u68af\u5ea6\u7206\u70b8&#xff1a;RNN\u8bad\u7ec3\u65f6\u5bb9\u6613\u51fa\u73b0\u7684\u95ee\u9898&#xff0c;LSTM\u7684\u95e8\u673a\u5236\u6b63\u662f\u4e3a\u7f13\u89e3\u68af\u5ea6\u6d88\u5931\u800c\u8bbe\u8ba1\u3002<\/li>\n<\/ul>\n<h3>\u4f18\u7f3a\u70b9\u4e0e\u9002\u7528\u573a\u666f<\/h3>\n<p>\u4f18\u70b9&#xff1a;\u5929\u751f\u5904\u7406\u5e8f\u5217\u3001\u6709\u8bb0\u5fc6\u3001\u80fd\u6355\u6349\u524d\u540e\u4f9d\u8d56&#xff0c;LSTM\/GRU\u8fd8\u89e3\u51b3\u4e86\u957f\u671f\u4f9d\u8d56&#xff1b;\u8f93\u5165\u8f93\u51fa\u5f62\u5f0f\u7075\u6d3b\u3002\u7f3a\u70b9&#xff1a;\u5fc5\u987b\u4e00\u6b65\u6b65\u987a\u5e8f\u8ba1\u7b97&#xff0c;\u6ca1\u6cd5\u5e76\u884c&#xff0c;\u8bad\u7ec3\u6162&#xff1b;\u5e8f\u5217\u7279\u522b\u957f\u65f6\u4f9d\u7136\u5403\u529b&#xff1b;\u8fd9\u4e24\u70b9\u6b63\u662f\u540e\u6765Transformer\u8981\u9769\u5b83\u547d\u7684\u5730\u65b9\u3002<\/p>\n<p>\u9002\u5408\u573a\u666f&#xff1a;\u4e2d\u7b49\u957f\u5ea6\u7684\u5e8f\u5217\u4efb\u52a1&#xff08;\u6587\u672c\u5206\u7c7b\u3001\u65f6\u5e8f\u9884\u6d4b\u3001\u8bed\u97f3\u8bc6\u522b\u3001\u7b80\u5355\u7684\u7ffb\u8bd1&#xff09;\u3001\u6570\u636e\u91cf\u548c\u5e8f\u5217\u957f\u5ea6\u9002\u4e2d\u7684\u573a\u666f\u3002\u4e0d\u9002\u5408&#xff1a;\u8d85\u957f\u5e8f\u5217\u3001\u8ffd\u6c42\u6781\u81f4\u6548\u679c\u548c\u8bad\u7ec3\u901f\u5ea6\u7684\u5927\u89c4\u6a21\u4efb\u52a1&#xff0c;\u90a3\u4e9b\u5982\u4eca\u662fTransformer\u7684\u5929\u4e0b\u3002<\/p>\n<p>\u8981\u7279\u522b\u8bf4\u660e\u5b83\u4eca\u5929\u7684\u5b9a\u4f4d&#xff1a;\u5728Transformer\u51fa\u73b0\u540e&#xff0c;RNN\/LSTM\u5728\u5f88\u591a\u524d\u6cbf\u4efb\u52a1&#xff08;\u5c24\u5176\u5927\u89c4\u6a21\u7684NLP&#xff09;\u4e0a\u5df2\u88ab\u53d6\u4ee3\u3002\u4f46\u5b83\u4eec\u4f5c\u4e3a\u5e8f\u5217\u5efa\u6a21\u7684\u7ecf\u5178\u601d\u60f3&#xff0c;\u662f\u7406\u89e3Transformer\u7684\u91cd\u8981\u53f0\u9636&#xff08;Transformer\u6b63\u662f\u4e3a\u4e86\u89e3\u51b3RNN\u4e0d\u80fd\u5e76\u884c\u3001\u957f\u4f9d\u8d56\u96be\u6293\u8fd9\u4e24\u4e2a\u75db\u70b9\u800c\u751f&#xff09;&#xff0c;\u800c\u4e14\u5728\u4e00\u4e9b\u8f7b\u91cf\u3001\u8d44\u6e90\u53d7\u9650\u6216\u5e8f\u5217\u4e0d\u957f\u7684\u573a\u666f&#xff0c;LSTM\/GRU\u4f9d\u7136\u5b9e\u7528\u3002<\/p>\n<h3>\u5c0f\u7ed3\u4e0e\u627f\u4e0a\u542f\u4e0b<\/h3>\n<ul>\n<li>RNN&#xff1a;\u8fb9\u8bfb\u8fb9\u8bb0&#xff0c;\u7528\u80fd\u6cbf\u5e8f\u5217\u4f20\u9012\u7684\u9690\u85cf\u72b6\u6001&#xff08;\u8bb0\u5fc6&#xff09;\u5904\u7406\u524d\u540e\u6709\u4f9d\u8d56\u7684\u5e8f\u5217\u6570\u636e\u3002<\/li>\n<li>\u5065\u5fd8\u6bdb\u75c5&#xff1a;\u8bb0\u5fc6\u4e00\u6b65\u6b65\u4f20\u9012\u4f1a\u88ab\u7a00\u91ca&#xff0c;\u5e8f\u5217\u957f\u4e86\u5fd8\u6389\u524d\u9762\u7684&#xff08;\u957f\u671f\u4f9d\u8d56\u95ee\u9898&#xff09;\u3002<\/li>\n<li>LSTM\/GRU&#xff1a;\u7528\u9057\u5fd8\u95e8\u3001\u8f93\u5165\u95e8\u3001\u8f93\u51fa\u95e8\u52a0\u4e00\u6761\u8bb0\u5fc6\u4f20\u9001\u5e26&#xff0c;\u4e3b\u52a8\u51b3\u5b9a\u8bb0\u4ec0\u4e48\u5fd8\u4ec0\u4e48&#xff0c;\u6cbb\u597d\u5065\u5fd8\u3002<\/li>\n<li>\u5b9a\u4f4d&#xff1a;\u5e8f\u5217\u5efa\u6a21\u7ecf\u5178&#xff0c;\u88abTransformer\u53d6\u4ee3\u4f46\u4ecd\u662f\u7406\u89e3\u5b83\u7684\u91cd\u8981\u53f0\u9636&#xff0c;\u8f7b\u91cf\u548c\u8d44\u6e90\u53d7\u9650\u573a\u666f\u4ecd\u7136\u5b9e\u7528\u3002<\/li>\n<\/ul>\n<p>RNN\u5bb6\u65cf\u6709\u4e2a\u7ed5\u4e0d\u5f00\u7684\u786c\u4f24&#xff0c;\u5fc5\u987b\u4e00\u6b65\u6b65\u987a\u5e8f\u7b97\u3001\u6ca1\u6cd5\u5e76\u884c&#xff0c;\u5e8f\u5217\u4e00\u957f\u53c8\u6162\u53c8\u96be\u3002\u6709\u6ca1\u6709\u529e\u6cd5\u8ba9\u7f51\u7edc\u4e00\u773c\u770b\u5168\u6574\u4e2a\u5e8f\u5217\u3001\u8fd8\u80fd\u5e76\u884c\u52a0\u901f&#xff1f;\u8fd9\u4e2a\u95ee\u9898\u7684\u7b54\u6848&#xff0c;\u6380\u8d77\u4e86\u6574\u4e2aAI\u7684\u65b0\u6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